Power electronic conversion is a central element of energy conversion systems, acting as the interface between different forms of electrical energy and is an enabling technology for low-carbon economy. By 2030, it is expected that as much as 80% of all electric power will use Power Electronics somewhere within the energy supply chain between generation and consumption. Multilevel converter is one type of power electronics converter and can offer substantial benefits over the established two-level converter counterparts. These include reduced harmonic distortion, less voltage/current stress (dv/dt, di/dt), and mitigated electro-magnetic interference (EMI). Multilevel converters can be classified into multilevel voltage source converters (MVSCs) and multilevel current source converters (MCSCs). The MCSC exhibits several unique advantageous characteristics that may favour its adoption in the low voltage converters used in aircraft, hybrid/electric vehicles and micro renewable power generation. For example, inductors are used as the main energy storage elements in MCSCs, which are more reliable than capacitors in MVSCs. The MCSC has intrinsic current limiting capability, which can be used to constrain fault currents as well as leaving more time for device over-current protection. It may prevent further damage or failure of other components and avoid fire due to over-current. The research into MCSCs is at its infancy. The project therefore will explore the MCSCs through converter topology derivation, modulation techniques, inductor current balancing, loss and efficiency evaluation, etc. The successful investigations of these challenges will reveal the benefits of the MCSCs and facilitate the wide application of the converter. The research will be carried out through modelling, control, simulation and experimental verification. The study of power converter topologies is valued as enabling research in power electronic systems. A breakthrough in MCSCs will benefit the UK world-leading aerospace industry and renewable energy OEMs. Successful development of MCSCs will also feed into the component or sub-system supply chain, in exploiting new power semiconductor technologies (reverse blocking IGBT, wide-bandgap devices) and in requiring new power module configurations and new wound components.

The aim of this project is to use statistical methods to develop "green button" manufacturing processes: processes that can be run without a human operator, and can respond to unpredictable variations in the properties of the materials that are being machined. We will be focussing on "high value, low volume" manufacturing: manufacturing relatively small numbers of very expensive components, where it is costly to have to scrap a component because of a fault in the machining process. We will work on a case study: machining the landing gear of an aircraft, which we will use to develop methods that can be applied more generally. The first step will be to build a computer model of the machining process. Given the computer model, we can experiment with different parameters of the machining process such as the speed at which the metal is cut, and the path that the cutting tool takes through the metal. In theory, we could then search for the best choice of parameters, such that the component is machined in the shortest time and is least likely to be defective. However, the properties of the metal to be cut will vary from item to item, so what is best for one item may not be best for another. We can't measure all the relevant properties, so we need to first assess how much variability we are likely to see, and then find parameter settings that best able to handle this variability without producing faulty items. Once we have determined the best parameter settings, we will then run a small number of machine cutting tests at different choices of machine cutting parameters. During these tests, we will take high quality but expensive measurements, telling us for example, the temperatures and forces exerted on the cutting tools. This information will tell us whether the process is operating satisfactorily, or whether there is a risk of tool damage and possibly a faulty machined component. We will also take lower quality, cheaper, sensor measurements, of the sort that would be available during the manufacturing process in the factory. We will study the relationship between all the variables that we have measured, so that we can construct a simulation model of the entire manufacturing process. (We can also make corrections to the computer model predictions, by inspecting how well the computer model predicts the cutting test outcomes). We can then use the simulation model to explore different strategies for modifying the process mid-production, in response to the cheaper sensor data, to avoid faults (eg "reduce the cutting speed by 10%" if a sensor reports vibration 5% above average"). It will be cheaper and faster to design the automated process using the simulation model, rather than conducting more expensive cutting tests. The end product will be a manufacturing process that can run efficiently without a human operator, making adjustments as the sensor data are observed, and will be configured in such a way so that it can deal with variability in the properties of the items to be machined. Our aim is to produce statistical methodology for configuring such a process, that can be applied in many different settings.

This proposal is focused on two areas of the control of properties of Ti alloys processed via the ingot-route and via powder, both of which should lead to high strength, high toughness alloys: (i) the addition of carbon to Ti alloys, which reduces grain boundary alpha and (ii) the application of Net Shape HIPping to produce components directly from powder, where HIPping can increase the toughness by a factor of two, coupled with a slight improvement in tensile strength.The main aim of the first area of work is to investigate using ingot-route samples, whether carbon can have the same effect on grain boundary and matrix alpha in alloys such as Ti6246 and Ti5553 as that found in Ti-15-3; grain boundary precipitation is virtually eliminated and matrix precipitation of alpha is refined. Addition of carbon to Ti5553, coupled with optimisation of the heat treatment, could lead to an alloy with a tensile strength of over 1000MPa and a fracture toughness approaching 100MPavm. Improvements in Ti6246 would hasten the replacement of Ti64 in engine applications. For quenched alloys, such as Ti-15-3, grain boundary alpha is formed during ageing. In order to limit the boundary alpha in Ti-15-3, solution treatment is carried out at temperatures where a significant density of crystal defects which are formed during processing, remain, or samples are deformed after quenching and the dislocations introduced act as nucleation sites for alpha. Because dislocations are distributed heterogeneously this is not satisfactory. The solution treatment of Ti5553 is usually carried out below the beta transus, so that some globular alpha remains, much of it on grain boundaries. This boundary alpha is important, since it limits grain growth during solution treatment, but its presence and the presence of any additional grain boundary alpha formed during cooling and ageing limits the fracture toughness of thermomechanically processed samples.The specific objectives of this first part are to understand the precipitation behaviour (and to establish if precipitation involve vacancies) and to investigate and understand the tensile and fracture properties in ingot-route samples of solution treated and aged C-free and C-containing Ti153, Ti5553 and Ti6246.The second area is focused on the properties of as-HIPped powder samples. The development of optimised properties during processing of components produced via powder processing has become of increasing significance recently for two reasons. Firstly, the production of structural components via Net Shape HIPping (i.e. the production of a near net shape component from powder in a single HIPping operation) is becomingly increasingly important for a number of reasons; as-HIPped components will thus be competing with forged components and the reliability, reproducibility and level of their properties must be guaranteed. The second reason why the properties of as-HIPped components have become an important area requiring research and development, is the fact that it has been found that Ti6Al4V, in the as-HIPped condition, can show an increase in fracture toughness of nearly a factor of two, over those typical for thermomechanically processed samples. This remarkable improvement is coupled with small improvements in other properties.The aim of this part of the project is to understand the factors that control the microstructure of as-HIPped powder processed samples of Ti6246 and Ti5553 and thus to understand the factors that control the fracture behaviour of as-HIPped powder-processed samples of Ti6246 and Ti5553. The overall aims of the project are thus to develop an understanding of this improvement in properties of ingot-route and powder-route Ti alloys and to use this understanding to optimise the processing for both alpha-stabilised and beta-stabilised alloys and to assess whether addition of carbon to alloys would lead to further improvements in the fracture properties especially of beta-stabilised alloy

One of the main contributors towards the cost of high-value engineering assets is the cost of maintenance. Taking an aircraft out of service for inspection means loss of revenue. However, the alternative - allowing damage to remove the aircraft from service - is much more undesirable with cost and safety being issues. In terms of an offshore wind farm, the cost of an unscheduled visit to a remote site to potentially replace a 75m blade hardly bears thinking about. If one can adopt a condition-based approach to maintenance where the structure of interest is monitored constantly by permanent sensors and data processing algorithms alert the owner or user when damage is developing, one can optimise the maintenance program for cost without sacrificing safety. If incipient damage is detected, repair rather than replacement can be a viable option. Unfortunately, the complexity of modern structures together with the challenging environments in which they operate makes it very difficult to develop data-processing algorithms which can detect and identify incipient damage. The discipline concerned with these problems - structural health monitoring (SHM) - suffers from serious problems which have prevented uptake of the technology by industry. The structural complexity makes analysis difficult; however, one variant of SHM - the data-based approach - shows promise in this respect. In this case one learns directly from data from the structure using pattern recognition techniques to diagnose different levels of damage. Sadly, data-based SHM has its own problems; the first is that most pattern recognition approaches to SHM require one to measure data from the structure in all possible states of damage. In the case of a structure like an aircraft - consider the A380 - it is simply not conceivable that one should damage a single one for data collection purposes, let alone many. Fortunately, if one is only interested simply in whether damage is present or not, this can be accomplished using only data from the healthy condition. One builds a picture of the healthy state of the structure and then monitors for deviations from this state. This raises the second major issue with data-based SHM; if one is monitoring the structure for changes, one does not wish to raise an alarm because of a benign change in its environmental or operational conditions; these are termed 'confounding influences'. The solution may lie within the healthcare informatics community. A field called 'syndromic surveillance' (SS) has arisen over the last 20 years concerned with fast detection of disease outbreaks by monitoring human populations. The data themselves can be very different, from over-the-counter medicine sales to numbers of hits on health advice websites. The data are fused together and analysed to give a spatio-temporal picture of public health and alerting algorithms similar to the ones used for SHM can be used to warn healthcare professionals that an epidemic may be on the way. The ideas have even been embedded in software, the prime example being the ESSENCE II system which keeps a watchful eye over three US states. The current proposal aims to develop a SS system for engineering structures with the capability of fast detection and location for faults on high-value assets. The population-based approach to SHM proposed here has the potential to solve the two problems discussed above. If many structures are monitored, inferences between structures can potentially avoid the need for very detailed knowledge of individual structures. As structures fail with time, the knowledge of damage states will build. In terms of the second problem, SS systems have always dealt with confounding influences and can provide inspiration for new algorithms for data-based SHM. As in the case of ESSENCE II; the system will be embedded in software so that multiple operators of structures can derive maximum benefit from the diagnostic capability of the population-based system.

Step changes in electrical machine (e-machine) performance are central to the success of future More-Electric and All-Electric transport initiatives and play a vital role in meeting the UK's Net Zero Emission target by 2050. E-machine technology roadmaps from the Advanced Propulsion Centre (APC) and Aerospace Technology Institute (ATI) seek continuous power-density of between 9 and 25 kW/kg by 2035, in stark contrast to the 2-5 kW/kg available today. E-machine power-density is ultimately limited by the ability to dissipate internally generated losses, which manifest as heat, and the temperature rating of the electrical insulation system. The electrical conductors, referred to as windings, are often the dominant loss source and are conventionally formed from electrically insulated copper or aluminium conductors. Such conductors are manufactured using a drawing and insulation technique, which aside from improvements in materials, has seen little change in the past century. Exploring alternative manufacturing methods could allow reduction in losses, enhanced heat extraction and facilitate increased temperature ratings, ushering the necessary step changes in power-density and e-machine performance. Metal Additive Manufacturing (AM) is a process in which material is selectively bonded layer by layer to ultimately form a 3D part, enabling complex parts to be produced which may not be feasible using conventional methods. The design freedom offered by AM provides much sought-after opportunities to simultaneously reduce winding losses and packaging volume, improve thermal management and enable the use of high-temperature electrical insulation coatings. The design of such windings requires the development of new multi-physics design tools accounting for electromagnetic, thermo- and fluid- dynamics, mechanical and Design for AM (DfAM) aspects. It is important to have an understanding of the AM process, including the resulting material properties of parts and limitations on feature sizes and geometry in order to fully exploit the design freedoms whilst ensuring manufacturing feasibility. Establishing how to use build-supports and post-processes to improve component surface quality and facilitate application of electrical insulation coatings is another important aspect. To this end, I conducted initial studies in collaboration with academic and industrial partners focusing on shaped profile windings which have demonstrated the potential benefits of metal AM in e-machines and the drastic expansion of design possibilities to be explored. I intend to expand on this initial work through this fellowship which will provide me with flexible funding over a 4 + 3 year term to support The Electrical Machine Works, an ambitious and comprehensive research programme reminiscent of a Skunk Works project which draws together UK industry and academic expertise in AM, material science and multi-physics e-machine design to establish an internationally leading platform in this important emerging field. It is envisaged that the fellowship and associated platform, The Electrical Machine Works, will facilitate interdisciplinary collaboration with both industry and academia, catalysing high quality academic outputs disseminated through appropriate conference and journal publications, and the generation of Intellectual Property (IP), helping to keep the UK competitive in Power Electronics Machines and Drives (PEMD) and at the forefront of this area. If successful, in time The Electrical Machine Works will become a centre of excellence for AM in e-machines, contributing to a future skills and people pipeline and aiding in the raising of Technology Readiness Levels (TRL) in line with national priorities as expressed by the UK's Industrial Strategy, Advanced Propulsion Centre (APC), Aerospace Technology Institute (ATI) and Industrial Strategy Challenge Fund (ISCF) Driving the Electric Revolution (DER) and Future Flight (FF) initiatives.